Antelop

A database for systems neuroscience foundation models

Rory Bedford

MRC LMB

Foundation models in neuroscience

Large machine learning models that learn the relationship between neural activity, sensory inputs and motor outputs
Trained via self-supervised learning methods such as masked modelling
Can be fine tuned to perform a number of prediction tasks
Require extremely large datasets, well structured and preprocessed

Increasingly large datasets within systems neuroscience (e.g. Neuropixels)
Data storage needs to keep up with data engineering best practices
Custom file formats/project structures are hard to parse
Data preprocessing pipelines becoming increasingly complex and computationally expensive
Custom preprocessing/analysis scripts are very difficult to reproduce
High entry barrier to existing tools like DataJoint and NWB which makes their adoption difficult for many labs

Software package designed to facilitate the easy adoption of data processing and storage best practices
Simple pip install and straightforward graphical configuration
Extensive graphical user interface for all aspects of your data management and processing
MySQL database backend for centralised storage
Supports electrophysiology, calcium imaging, and behavioural data processing with HPC integration

Integrates with existing tools, such as popular spikesorters, CaImAn, and DeepLabCut
Implements a range of data visualisation tools and metrics out of the box, including an analysis standard library
Supports the writing of custom analysis scripts, with direct integration to your lab’s GitHub and data immutability checks for reproducibility
Has import/export functions for NWB and a range of acquisition systems
Has an opinionated but accomodating database structure for ML models to utilise

Thank you